What it takes to be a Data Scientist in 2018 and where does the skills gap lie?

27 Jul 2018

Analytics

Skills

Data

That there are skill shortages in the market is hardly news. The ever-rising tide of data science salaries revealed each year in the IAPA 2017 Skills & Salary Survey results provide evidence of that.

“However, it is possible to overstate the problem,” suggests Simon Rumble, co-founder at Snowflake Analytics

“Data science is a bit of a buzzword — the joke is that a data scientist is a statistician with a Mac. In reality, it’s someone with both stats knowledge and the ability to program and manipulate data sets,” he says.

“There’s actually plenty of new graduates who took the right subjects and have the right attitude to become data scientists, so hats-off to the universities for finally providing what’s in demand.”

However, Simon acknowledges that there remains a big gap when it comes to finding people with real-world experience. “If they go straight into a business as the sole data scientist or analyst with no mentoring or supervision, everyone’s going to have a bad time. “Mentorship and internship programs are really important.”

Getting the right mix of soft and hard skills is also problematic.

Many data science leaders say that getting the soft skills right is more important than ticking off a set of technical capabilities — which are more likely to be considered table stakes for employment.

Curiosity, a logical mind and good communication skills, as well as a level of flexibility, are seen as critical. The latter especially — because when it comes to real-world data, nothing is ever optimal.

Analysts who can’t adjust to dealing with poor-quality or sparse data will struggle, according to data analytics executives. “The technology environment changes so fast that you’re much better served to have someone with the right thought processes and the ability to learn new skills and techniques, than a specialist who isn’t flexible enough to move with changes in the environment,” says Anna Russell, Director at Polynomial Solutions. Anna also cites common sense as an important skill.

“As data volumes get bigger and tools more sophisticated, it’s really easy to go down a rabbit hole of ‘what ifs’ that waste time and lead nowhere. So many people get hung up on the technique that they forget about the application.”

Rumble, meanwhile, says an analytics team also needs a champion willing to build pilots of the test-and-learn process through the business, sell it in, trumpet the successes and the failures, try to bring people along.

For more insights and trends for data analytics professionals in 2018, download IAPA’s recent research paper: Hit the Accelerator